Reducing hospital readmissions is a national healthcare priority. In October 2012, the Centers for Medicare and Medicaid Services (CMS) enacted financial penalties on hospitals with higher than average risk‐adjusted readmissions, offering an incentive pool of $850 million in the first year.[1] As a result, a wide range of activities to understand and reduce readmissions among patients with congestive heart failure (CHF), acute myocardial infarction (AMI), and pneumonia have emerged.[2, 3, 4, 5] Some have proposed that to effectively reduce hospital readmissions, a community of inpatient and outpatient providers and local support organizations must coordinate efforts.[6, 7] In fact, CMS has recognized the important role of community support groups and outpatient providers in safe discharge transitions in 2 ways. First, in 2011 CMS launched the Community‐based Care Transitions Program to fund community‐based organizations to assist Medicare patients with care transitions.[8] Second, CMS introduced 2 new reimbursement codes for primary care providers (PCPs) to perform care coordination immediately after hospital discharge.[9] Both of these new payment programs represent an evolving perspective that reducing hospital readmissions requires active participation among outpatient partners.

As leaders of the outpatient care team, PCPs play a significant role in reducing hospital readmissions. One way in which PCPs can begin to understand the magnitude of the issue within their clinic is to evaluate the clinic's 30‐day readmission rates. Currently, CMS calculates readmission rates at the hospital level. However, understanding these rates at the clinic level is critical for developing strategies for improvement across the care continuum. Our current understanding of effective outpatient interventions to reduce hospital readmission is limited.[3] As clinics introduce and refine strategies to reduce readmissions, tracking the impact of these strategies on readmission rates will be critical for identifying effective outpatient interventions. Clinics with similar patient case‐mix can also benchmark readmission rates, sharing best practices from clinics with lower‐than‐expected rates.

There are no available studies or proposed methodologies to guide primary care clinics in calculating their 30‐day readmission rates. A particularly difficult challenge is obtaining the admission information when patients may be admitted to 1 of several area hospitals. For large integrated delivery networks where primary care patients are relatively loyal to the network of physicians and hospitals, an opportunity exists to explore the data. Furthermore, variations in readmission rates across primary care specialties (such as internal medicine and family practice) are not well understood. In this study, we set out to develop a methodology for calculating all‐cause 30‐day hospital readmission rates at the level of individual primary care practices and to identify factors associated with variations in these rates.

METHODS

RESULTS

During the study period, there were 12,564 discharges from UCSF Medical Center for primary care patients belonging to the 7 clinics. Of these, 8685 were index discharges and 1032 were readmissions within 30 days. Table 1 shows the characteristics of the patients who had at least 1 admission during the study period. In all but 2 clinics (HIV Primary Care and Concierge Internal Medicine), there were more women hospitalized than men. Age and gender differences across clinics are consistent with the patient populations served by these clinics, with Women's Health having more female patients and younger patients hospitalized compared to Geriatrics.

All‐cause 30‐day readmission rates varied across practices, with HIV Primary Care being the highest at 14.9%, followed by Geriatrics at 13.7%, General Internal Medicine at 13.3%, Concierge Internal Medicine at 9.7%, combined IM/FP at 8.9%, Family Practice at 8.6%, and Women's Health at 7.7% (Figure 1). Despite HIV Primary Care having the highest readmission rate, the number of index discharges during the 3‐year period was relatively low (249) compared to General Internal Medicine (5388). For the index admission, the top 5 admitting services were medicine, obstetrics, cardiology, orthopedic surgery, and general surgery (Table 2). Medicine was the primary admitting service for patients in the following clinics: General Internal Medicine, Geriatrics, HIV Primary Care, and Concierge Internal Medicine. Obstetrics was the primary admitting service for patients in the Family Practice, Women's Health, and combined IM/FP clinics. Vaginal delivery was the top discharge diagnosis‐related group (DRG) for patients in the General Internal Medicine, Family Practice, Women's Health, and combined IM/FP clinics. The top discharge DRG was urinary tract infection for Geriatrics, HIV for the HIV clinic (13.6%), and chemotherapy (8.0%) for Concierge Internal Medicine. Average LOS varied from 4.7 days for patients in the HIV Primary Care clinic to 2.8 days for patients in the Concierge Internal Medicine clinic. Average LOS was 3.4, 3.1, and 3.2 days in the Family Practice, Women's Health, and Geriatrics clinics, respectively, and 3.8 days in the General Internal Medicine and combined IM/FP clinics. For all clinics except Geriatrics, the majority of patients were discharged home without home health. A larger proportion of patients in the Geriatrics clinic were discharged home with home health or discharged to a skilled nursing facility.

Figure 1

Factors associated with variation in readmission rates included: male gender (OR: 1.21, 95% CI: 1.051.40), Medicare (OR: 1.31, 95% CI: 1.051.64; Ref=private) and dual‐eligible Medicare‐Medicaid insurance (OR: 1.26, 95% CI: 1.011.56), unknown primary language (OR: 0.06, 95% CI: 0.020.25; Ref=English), and the following comorbidities: peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Multivariable logistic regression modeling results are listed in Table 3. Patients having a resident PCP showed no increased odds of readmission (OR: 1.13, 95% CI: 0.931.37; Ref=attending PCP). However, patients with a graduated resident PCP or departed faculty PCP awaiting transfer to a new PCP had an OR of 1.59 (95% CI: 1.162.17) compared with having a current faculty PCP. The C‐statistic for this model was 0.67.

DISCUSSION

In this study we introduce a complementary way to view hospital readmissions, from the perspective of primary care practices. We found variation in readmission rates across primary care practices. After controlling for admitting service, clinic, provider, and patient factors, the specific characteristics of male gender, patients with Medicare or Medicare with Medicaid, and patients in our General Internal Medicine clinic with a departed PCP were independent risk factors for hospital readmission. Patients with specific comorbidities were also at increased risk for admissions, including those with peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Because this is the first study viewing readmissions from the perspective of primary care practices, our findings are unique in the literature. However, hospital‐based studies have found similar relationships between readmission rates and these specific comorbidities.[11, 12, 15] Unlike other studies, our study cohort did not show CHF as an independent risk factor. We hypothesize this is because in 2008, UCSF Medical Center introduced an inpatient‐based intervention to reduce readmissions in patients with heart failure. By 2011, the readmission rates of patients with heart failure (primary or secondary diagnoses) had dropped by 30%. The success of this program, focused only on patients with heart failure, likely affected our analysis of comorbidities as risk factors for readmissions.

Models developed to predict hospital readmissions, overall and for specific disease conditions, have inconsistently identified predictive factors, and there is not a specific set of variables that dominate.[16, 17, 18, 19] A recent review of readmission risk prediction models suggested that models that take into account psychosocial factors such as social support, substance abuse, and functional status improved model performance.[16] We hypothesize that the reason why male gender was significant in our model may be related to lack of social support, especially among those who may be single or widowed. Other studies have also showed male gender as a predictor for hospital readmissions.[14, 20]

People with Medicare as the primary payor or Medicare with Medicaid also tended to have higher risk of readmission. We believe that this may be a proxy for the combined effect of age, multiple comorbidities, and psychosocial factors. In a multicenter study of general medicine inpatients, Medicare, but not age, was also found to be a predictive variable.[21] Unlike other studies, our study did not find Medicaid alone as a significant predictive variable for readmissions.[15, 21] One explanation may be that in hospital‐based studies, people with Medicaid who are at high risk for readmission may be high‐risk because they do not have a PCP or good access to outpatient care. In our study, all patients have a PCP in 1 of the UCSF clinics, and access to care is improved with this established relationship. These other studies did not examine Medicare‐Medicaid dual eligibility status. Our results are consistent with a national study on avoidable hospital admissions that showed the dual eligibility population experiencing 60% higher avoidable admission rates compared to the Medicaid‐only population.[22]

An interesting finding was that there were no statistically significant differences in readmissions among patients who report a language other than English as their primary language. Although language barriers and health literacy can affect a patient's ability to understand discharge instructions, the use of translators at UCSF Medical Center may have decreased the risk of readmissions. For a small number of patients whose primary language was not recorded (unknown language in our model), they appeared to have a lower risk of readmissions. Language preferences are recorded by our admitting staff during the process of admission. This step may have been skipped after hours or if the patient was not able to answer the question. However, we do not have a good hypothesis as to why these patients may have a reduced risk of readmissions.

Having a departed PCP in the General Internal Medicine clinic was an independent predictor of readmissions. These patients have access to primary care; they can schedule acute appointments with covering providers for new medical issues or follow‐up of chronic conditions. However, until they are transferred to a new PCP, they do not have a provider who is proactively managing their preventive and chronic disease care, including follow‐up and coordinating care after hospital discharge. Our study is not the first to suggest adverse outcomes for patients who are in transition from 1 primary care provider to the next.[23] Studies conducted in General Internal Medicine clinics have shown missed opportunities for cancer screening and overlooked test results during the transition period,[24] and as many as one‐fifth of patients whom residents identified as high‐risk were lost to follow‐up.[25] However, our study is the first to show the link between PCP transition in a teaching clinic and hospital readmissions. This finding underscores the importance of continuity of care in the optimal management of patients following hospital discharge.

There are several limitations to this study. First, our study only considers hospitalizations to UCSF Medical Center, potentially undercounting readmissions to area hospitals. Because our study population are patients with PCPs at UCSF, these patients tend to seek specialty and acute care at UCSF Medical Center as well. We obtained payor data from our medical group (Hill Physicians), which covers our largest private payors, to understand whether our results can be applied on a global basis. We found that in 87.4% of index admissions with readmissions from January 1, 2010 through May 30, 2012, the readmission occurred at UCSF Medical Center. We conducted a similar analysis with CMS data from October 1, 2008 to June 30, 2011, the latest data we have from CMS. For patients with UCSF PCPs and a diagnosis of AMI or CHF, 100% were readmitted back to UCSF Medical Center. For patients with UCSF PCPs and a diagnosis of pneumonia, 89% were readmitted back to UCSF Medical Center. Given that only a small percentage of patients with PCPs at UCSF present for readmission at other area hospitals, we believe that limiting our analysis to UCSF Medical Center is reasonable.

In our study, we did not remove vaginal deliveries or Caesarian sections prior to building the model. Primary care physicians and their clinic leadership are accustomed to taking a population‐health perspective. We anticipate they would be interested in designing interventions and addressing readmission for the entire primary care panel. Although readmissions after delivery are not frequent, they can still occur, and interventions should not necessarily exclude this population. We did run a sensitivity analysis by removing vaginal delivery and Caesarian sections from the analysis. As expected, readmission rates for all practices increased except for Geriatrics. However, the independent predictors of readmissions did not change.

Our study is based on a population of patients at 1 urban academic medical center and may not be generalizable across all delivery systems. Our population is racially and ethnically diverse, and many do not speak English as their primary language. The study also spans different types of primary care clinics, capturing a wide range of ages and case mix. As PCP assignments fluctuate over time, there may be errors with PCP attribution in the UCSF Medical Center data systems. We do not believe errors in PCP attribution would differ across primary care practices. Because the primary care practices' performance reports on quality measures are based on PCP assignment, each clinic regularly updates their clinic panels and has specific protocols to address errors with PCP attribution.

Finally, our study includes only the variables that we were able to extract from administrative claims. Other explanatory variables that have been suggested as important for evaluation, such as social support, functional assessment, access to care, hospital discharge process, and posthospitalization follow‐up, were not included. Each of these could be explored in future studies.

This study offers a unique perspective of hospital readmission by introducing a new methodology for primary care clinics to calculate and evaluate their all‐cause 30‐day readmission rates. Although this methodology is not intended to provide real‐time feedback to clinics on readmitted patients, it opens the door for benchmarking based on specific case‐mix indices. Another direction for future research is to design robust evaluations of the impact of interventions, both inpatient and outpatient, on primary care clinic readmission rates. Finally, future research should replicate this analysis across teaching clinics to identify whether provider turnover is a consistent independent predictor of hospital readmissions.

This study also has implications for inpatient interventions. For example, discharging physicians may consider proactively identifying whether the patient has a continuous primary care provider. Patients who are in between PCPs may need closer follow‐up after discharge, until they re‐establish with a new PCP. This can be accomplished through a postdischarge clinic visit, either run by inpatient providers or covering physicians in the primary care clinic. In addition, the discharging physician may work with the case manager to increase the level of care coordination. The case manager could contact the primary care clinic and proactively ask for immediate PCP re‐assignment. Once a new PCP has been identified, the discharging physician could consider a warm hand‐off. With a warm hand‐off, the new PCP may feel more comfortable managing problems that may arise after hospital discharge, and especially before the first outpatient visit with the new PCP. Future research can test whether these interventions could effectively reduce hospital readmissions across a broad primary care population.

CONCLUSION

Primary care providers and their clinics play an important role in managing population health, decreasing healthcare spending, and keeping patients out of the hospital. In this study, we introduce a tool in which primary care clinics can begin to understand their hospital readmission rates. This may be particularly valuable as primary care providers enter global payment arrangements such as accountable care organizations or bundled payments and are responsible for a population of patients across the continuum of care. We found significant variation in readmission rates between different primary care practices, but much of this variation appears to be due to differences between practices in patient demographics, comorbidities, and hospitalization factors. Our study is the first to show the association between provider transitions and higher hospital readmissions. Continuity of care is critical for the optimal management of patients following hospital discharge. More attention will need to be focused on providing good continuity outpatient care for patients at high risk for readmissions.

Reducing hospital readmissions is a national healthcare priority. In October 2012, the Centers for Medicare and Medicaid Services (CMS) enacted financial penalties on hospitals with higher than average risk‐adjusted readmissions, offering an incentive pool of $850 million in the first year.[1] As a result, a wide range of activities to understand and reduce readmissions among patients with congestive heart failure (CHF), acute myocardial infarction (AMI), and pneumonia have emerged.[2, 3, 4, 5] Some have proposed that to effectively reduce hospital readmissions, a community of inpatient and outpatient providers and local support organizations must coordinate efforts.[6, 7] In fact, CMS has recognized the important role of community support groups and outpatient providers in safe discharge transitions in 2 ways. First, in 2011 CMS launched the Community‐based Care Transitions Program to fund community‐based organizations to assist Medicare patients with care transitions.[8] Second, CMS introduced 2 new reimbursement codes for primary care providers (PCPs) to perform care coordination immediately after hospital discharge.[9] Both of these new payment programs represent an evolving perspective that reducing hospital readmissions requires active participation among outpatient partners.

As leaders of the outpatient care team, PCPs play a significant role in reducing hospital readmissions. One way in which PCPs can begin to understand the magnitude of the issue within their clinic is to evaluate the clinic's 30‐day readmission rates. Currently, CMS calculates readmission rates at the hospital level. However, understanding these rates at the clinic level is critical for developing strategies for improvement across the care continuum. Our current understanding of effective outpatient interventions to reduce hospital readmission is limited.[3] As clinics introduce and refine strategies to reduce readmissions, tracking the impact of these strategies on readmission rates will be critical for identifying effective outpatient interventions. Clinics with similar patient case‐mix can also benchmark readmission rates, sharing best practices from clinics with lower‐than‐expected rates.

There are no available studies or proposed methodologies to guide primary care clinics in calculating their 30‐day readmission rates. A particularly difficult challenge is obtaining the admission information when patients may be admitted to 1 of several area hospitals. For large integrated delivery networks where primary care patients are relatively loyal to the network of physicians and hospitals, an opportunity exists to explore the data. Furthermore, variations in readmission rates across primary care specialties (such as internal medicine and family practice) are not well understood. In this study, we set out to develop a methodology for calculating all‐cause 30‐day hospital readmission rates at the level of individual primary care practices and to identify factors associated with variations in these rates.

METHODS

RESULTS

During the study period, there were 12,564 discharges from UCSF Medical Center for primary care patients belonging to the 7 clinics. Of these, 8685 were index discharges and 1032 were readmissions within 30 days. Table 1 shows the characteristics of the patients who had at least 1 admission during the study period. In all but 2 clinics (HIV Primary Care and Concierge Internal Medicine), there were more women hospitalized than men. Age and gender differences across clinics are consistent with the patient populations served by these clinics, with Women's Health having more female patients and younger patients hospitalized compared to Geriatrics.

All‐cause 30‐day readmission rates varied across practices, with HIV Primary Care being the highest at 14.9%, followed by Geriatrics at 13.7%, General Internal Medicine at 13.3%, Concierge Internal Medicine at 9.7%, combined IM/FP at 8.9%, Family Practice at 8.6%, and Women's Health at 7.7% (Figure 1). Despite HIV Primary Care having the highest readmission rate, the number of index discharges during the 3‐year period was relatively low (249) compared to General Internal Medicine (5388). For the index admission, the top 5 admitting services were medicine, obstetrics, cardiology, orthopedic surgery, and general surgery (Table 2). Medicine was the primary admitting service for patients in the following clinics: General Internal Medicine, Geriatrics, HIV Primary Care, and Concierge Internal Medicine. Obstetrics was the primary admitting service for patients in the Family Practice, Women's Health, and combined IM/FP clinics. Vaginal delivery was the top discharge diagnosis‐related group (DRG) for patients in the General Internal Medicine, Family Practice, Women's Health, and combined IM/FP clinics. The top discharge DRG was urinary tract infection for Geriatrics, HIV for the HIV clinic (13.6%), and chemotherapy (8.0%) for Concierge Internal Medicine. Average LOS varied from 4.7 days for patients in the HIV Primary Care clinic to 2.8 days for patients in the Concierge Internal Medicine clinic. Average LOS was 3.4, 3.1, and 3.2 days in the Family Practice, Women's Health, and Geriatrics clinics, respectively, and 3.8 days in the General Internal Medicine and combined IM/FP clinics. For all clinics except Geriatrics, the majority of patients were discharged home without home health. A larger proportion of patients in the Geriatrics clinic were discharged home with home health or discharged to a skilled nursing facility.

Figure 1

Factors associated with variation in readmission rates included: male gender (OR: 1.21, 95% CI: 1.051.40), Medicare (OR: 1.31, 95% CI: 1.051.64; Ref=private) and dual‐eligible Medicare‐Medicaid insurance (OR: 1.26, 95% CI: 1.011.56), unknown primary language (OR: 0.06, 95% CI: 0.020.25; Ref=English), and the following comorbidities: peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Multivariable logistic regression modeling results are listed in Table 3. Patients having a resident PCP showed no increased odds of readmission (OR: 1.13, 95% CI: 0.931.37; Ref=attending PCP). However, patients with a graduated resident PCP or departed faculty PCP awaiting transfer to a new PCP had an OR of 1.59 (95% CI: 1.162.17) compared with having a current faculty PCP. The C‐statistic for this model was 0.67.

DISCUSSION

In this study we introduce a complementary way to view hospital readmissions, from the perspective of primary care practices. We found variation in readmission rates across primary care practices. After controlling for admitting service, clinic, provider, and patient factors, the specific characteristics of male gender, patients with Medicare or Medicare with Medicaid, and patients in our General Internal Medicine clinic with a departed PCP were independent risk factors for hospital readmission. Patients with specific comorbidities were also at increased risk for admissions, including those with peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Because this is the first study viewing readmissions from the perspective of primary care practices, our findings are unique in the literature. However, hospital‐based studies have found similar relationships between readmission rates and these specific comorbidities.[11, 12, 15] Unlike other studies, our study cohort did not show CHF as an independent risk factor. We hypothesize this is because in 2008, UCSF Medical Center introduced an inpatient‐based intervention to reduce readmissions in patients with heart failure. By 2011, the readmission rates of patients with heart failure (primary or secondary diagnoses) had dropped by 30%. The success of this program, focused only on patients with heart failure, likely affected our analysis of comorbidities as risk factors for readmissions.

Models developed to predict hospital readmissions, overall and for specific disease conditions, have inconsistently identified predictive factors, and there is not a specific set of variables that dominate.[16, 17, 18, 19] A recent review of readmission risk prediction models suggested that models that take into account psychosocial factors such as social support, substance abuse, and functional status improved model performance.[16] We hypothesize that the reason why male gender was significant in our model may be related to lack of social support, especially among those who may be single or widowed. Other studies have also showed male gender as a predictor for hospital readmissions.[14, 20]

People with Medicare as the primary payor or Medicare with Medicaid also tended to have higher risk of readmission. We believe that this may be a proxy for the combined effect of age, multiple comorbidities, and psychosocial factors. In a multicenter study of general medicine inpatients, Medicare, but not age, was also found to be a predictive variable.[21] Unlike other studies, our study did not find Medicaid alone as a significant predictive variable for readmissions.[15, 21] One explanation may be that in hospital‐based studies, people with Medicaid who are at high risk for readmission may be high‐risk because they do not have a PCP or good access to outpatient care. In our study, all patients have a PCP in 1 of the UCSF clinics, and access to care is improved with this established relationship. These other studies did not examine Medicare‐Medicaid dual eligibility status. Our results are consistent with a national study on avoidable hospital admissions that showed the dual eligibility population experiencing 60% higher avoidable admission rates compared to the Medicaid‐only population.[22]

An interesting finding was that there were no statistically significant differences in readmissions among patients who report a language other than English as their primary language. Although language barriers and health literacy can affect a patient's ability to understand discharge instructions, the use of translators at UCSF Medical Center may have decreased the risk of readmissions. For a small number of patients whose primary language was not recorded (unknown language in our model), they appeared to have a lower risk of readmissions. Language preferences are recorded by our admitting staff during the process of admission. This step may have been skipped after hours or if the patient was not able to answer the question. However, we do not have a good hypothesis as to why these patients may have a reduced risk of readmissions.

Having a departed PCP in the General Internal Medicine clinic was an independent predictor of readmissions. These patients have access to primary care; they can schedule acute appointments with covering providers for new medical issues or follow‐up of chronic conditions. However, until they are transferred to a new PCP, they do not have a provider who is proactively managing their preventive and chronic disease care, including follow‐up and coordinating care after hospital discharge. Our study is not the first to suggest adverse outcomes for patients who are in transition from 1 primary care provider to the next.[23] Studies conducted in General Internal Medicine clinics have shown missed opportunities for cancer screening and overlooked test results during the transition period,[24] and as many as one‐fifth of patients whom residents identified as high‐risk were lost to follow‐up.[25] However, our study is the first to show the link between PCP transition in a teaching clinic and hospital readmissions. This finding underscores the importance of continuity of care in the optimal management of patients following hospital discharge.

There are several limitations to this study. First, our study only considers hospitalizations to UCSF Medical Center, potentially undercounting readmissions to area hospitals. Because our study population are patients with PCPs at UCSF, these patients tend to seek specialty and acute care at UCSF Medical Center as well. We obtained payor data from our medical group (Hill Physicians), which covers our largest private payors, to understand whether our results can be applied on a global basis. We found that in 87.4% of index admissions with readmissions from January 1, 2010 through May 30, 2012, the readmission occurred at UCSF Medical Center. We conducted a similar analysis with CMS data from October 1, 2008 to June 30, 2011, the latest data we have from CMS. For patients with UCSF PCPs and a diagnosis of AMI or CHF, 100% were readmitted back to UCSF Medical Center. For patients with UCSF PCPs and a diagnosis of pneumonia, 89% were readmitted back to UCSF Medical Center. Given that only a small percentage of patients with PCPs at UCSF present for readmission at other area hospitals, we believe that limiting our analysis to UCSF Medical Center is reasonable.

In our study, we did not remove vaginal deliveries or Caesarian sections prior to building the model. Primary care physicians and their clinic leadership are accustomed to taking a population‐health perspective. We anticipate they would be interested in designing interventions and addressing readmission for the entire primary care panel. Although readmissions after delivery are not frequent, they can still occur, and interventions should not necessarily exclude this population. We did run a sensitivity analysis by removing vaginal delivery and Caesarian sections from the analysis. As expected, readmission rates for all practices increased except for Geriatrics. However, the independent predictors of readmissions did not change.

Our study is based on a population of patients at 1 urban academic medical center and may not be generalizable across all delivery systems. Our population is racially and ethnically diverse, and many do not speak English as their primary language. The study also spans different types of primary care clinics, capturing a wide range of ages and case mix. As PCP assignments fluctuate over time, there may be errors with PCP attribution in the UCSF Medical Center data systems. We do not believe errors in PCP attribution would differ across primary care practices. Because the primary care practices' performance reports on quality measures are based on PCP assignment, each clinic regularly updates their clinic panels and has specific protocols to address errors with PCP attribution.

Finally, our study includes only the variables that we were able to extract from administrative claims. Other explanatory variables that have been suggested as important for evaluation, such as social support, functional assessment, access to care, hospital discharge process, and posthospitalization follow‐up, were not included. Each of these could be explored in future studies.

This study offers a unique perspective of hospital readmission by introducing a new methodology for primary care clinics to calculate and evaluate their all‐cause 30‐day readmission rates. Although this methodology is not intended to provide real‐time feedback to clinics on readmitted patients, it opens the door for benchmarking based on specific case‐mix indices. Another direction for future research is to design robust evaluations of the impact of interventions, both inpatient and outpatient, on primary care clinic readmission rates. Finally, future research should replicate this analysis across teaching clinics to identify whether provider turnover is a consistent independent predictor of hospital readmissions.

This study also has implications for inpatient interventions. For example, discharging physicians may consider proactively identifying whether the patient has a continuous primary care provider. Patients who are in between PCPs may need closer follow‐up after discharge, until they re‐establish with a new PCP. This can be accomplished through a postdischarge clinic visit, either run by inpatient providers or covering physicians in the primary care clinic. In addition, the discharging physician may work with the case manager to increase the level of care coordination. The case manager could contact the primary care clinic and proactively ask for immediate PCP re‐assignment. Once a new PCP has been identified, the discharging physician could consider a warm hand‐off. With a warm hand‐off, the new PCP may feel more comfortable managing problems that may arise after hospital discharge, and especially before the first outpatient visit with the new PCP. Future research can test whether these interventions could effectively reduce hospital readmissions across a broad primary care population.

CONCLUSION

Primary care providers and their clinics play an important role in managing population health, decreasing healthcare spending, and keeping patients out of the hospital. In this study, we introduce a tool in which primary care clinics can begin to understand their hospital readmission rates. This may be particularly valuable as primary care providers enter global payment arrangements such as accountable care organizations or bundled payments and are responsible for a population of patients across the continuum of care. We found significant variation in readmission rates between different primary care practices, but much of this variation appears to be due to differences between practices in patient demographics, comorbidities, and hospitalization factors. Our study is the first to show the association between provider transitions and higher hospital readmissions. Continuity of care is critical for the optimal management of patients following hospital discharge. More attention will need to be focused on providing good continuity outpatient care for patients at high risk for readmissions.

Reducing hospital readmissions is a national healthcare priority. In October 2012, the Centers for Medicare and Medicaid Services (CMS) enacted financial penalties on hospitals with higher than average risk‐adjusted readmissions, offering an incentive pool of $850 million in the first year.[1] As a result, a wide range of activities to understand and reduce readmissions among patients with congestive heart failure (CHF), acute myocardial infarction (AMI), and pneumonia have emerged.[2, 3, 4, 5] Some have proposed that to effectively reduce hospital readmissions, a community of inpatient and outpatient providers and local support organizations must coordinate efforts.[6, 7] In fact, CMS has recognized the important role of community support groups and outpatient providers in safe discharge transitions in 2 ways. First, in 2011 CMS launched the Community‐based Care Transitions Program to fund community‐based organizations to assist Medicare patients with care transitions.[8] Second, CMS introduced 2 new reimbursement codes for primary care providers (PCPs) to perform care coordination immediately after hospital discharge.[9] Both of these new payment programs represent an evolving perspective that reducing hospital readmissions requires active participation among outpatient partners.

As leaders of the outpatient care team, PCPs play a significant role in reducing hospital readmissions. One way in which PCPs can begin to understand the magnitude of the issue within their clinic is to evaluate the clinic's 30‐day readmission rates. Currently, CMS calculates readmission rates at the hospital level. However, understanding these rates at the clinic level is critical for developing strategies for improvement across the care continuum. Our current understanding of effective outpatient interventions to reduce hospital readmission is limited.[3] As clinics introduce and refine strategies to reduce readmissions, tracking the impact of these strategies on readmission rates will be critical for identifying effective outpatient interventions. Clinics with similar patient case‐mix can also benchmark readmission rates, sharing best practices from clinics with lower‐than‐expected rates.

There are no available studies or proposed methodologies to guide primary care clinics in calculating their 30‐day readmission rates. A particularly difficult challenge is obtaining the admission information when patients may be admitted to 1 of several area hospitals. For large integrated delivery networks where primary care patients are relatively loyal to the network of physicians and hospitals, an opportunity exists to explore the data. Furthermore, variations in readmission rates across primary care specialties (such as internal medicine and family practice) are not well understood. In this study, we set out to develop a methodology for calculating all‐cause 30‐day hospital readmission rates at the level of individual primary care practices and to identify factors associated with variations in these rates.

METHODS

RESULTS

During the study period, there were 12,564 discharges from UCSF Medical Center for primary care patients belonging to the 7 clinics. Of these, 8685 were index discharges and 1032 were readmissions within 30 days. Table 1 shows the characteristics of the patients who had at least 1 admission during the study period. In all but 2 clinics (HIV Primary Care and Concierge Internal Medicine), there were more women hospitalized than men. Age and gender differences across clinics are consistent with the patient populations served by these clinics, with Women's Health having more female patients and younger patients hospitalized compared to Geriatrics.

All‐cause 30‐day readmission rates varied across practices, with HIV Primary Care being the highest at 14.9%, followed by Geriatrics at 13.7%, General Internal Medicine at 13.3%, Concierge Internal Medicine at 9.7%, combined IM/FP at 8.9%, Family Practice at 8.6%, and Women's Health at 7.7% (Figure 1). Despite HIV Primary Care having the highest readmission rate, the number of index discharges during the 3‐year period was relatively low (249) compared to General Internal Medicine (5388). For the index admission, the top 5 admitting services were medicine, obstetrics, cardiology, orthopedic surgery, and general surgery (Table 2). Medicine was the primary admitting service for patients in the following clinics: General Internal Medicine, Geriatrics, HIV Primary Care, and Concierge Internal Medicine. Obstetrics was the primary admitting service for patients in the Family Practice, Women's Health, and combined IM/FP clinics. Vaginal delivery was the top discharge diagnosis‐related group (DRG) for patients in the General Internal Medicine, Family Practice, Women's Health, and combined IM/FP clinics. The top discharge DRG was urinary tract infection for Geriatrics, HIV for the HIV clinic (13.6%), and chemotherapy (8.0%) for Concierge Internal Medicine. Average LOS varied from 4.7 days for patients in the HIV Primary Care clinic to 2.8 days for patients in the Concierge Internal Medicine clinic. Average LOS was 3.4, 3.1, and 3.2 days in the Family Practice, Women's Health, and Geriatrics clinics, respectively, and 3.8 days in the General Internal Medicine and combined IM/FP clinics. For all clinics except Geriatrics, the majority of patients were discharged home without home health. A larger proportion of patients in the Geriatrics clinic were discharged home with home health or discharged to a skilled nursing facility.

Figure 1

Factors associated with variation in readmission rates included: male gender (OR: 1.21, 95% CI: 1.051.40), Medicare (OR: 1.31, 95% CI: 1.051.64; Ref=private) and dual‐eligible Medicare‐Medicaid insurance (OR: 1.26, 95% CI: 1.011.56), unknown primary language (OR: 0.06, 95% CI: 0.020.25; Ref=English), and the following comorbidities: peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Multivariable logistic regression modeling results are listed in Table 3. Patients having a resident PCP showed no increased odds of readmission (OR: 1.13, 95% CI: 0.931.37; Ref=attending PCP). However, patients with a graduated resident PCP or departed faculty PCP awaiting transfer to a new PCP had an OR of 1.59 (95% CI: 1.162.17) compared with having a current faculty PCP. The C‐statistic for this model was 0.67.

DISCUSSION

In this study we introduce a complementary way to view hospital readmissions, from the perspective of primary care practices. We found variation in readmission rates across primary care practices. After controlling for admitting service, clinic, provider, and patient factors, the specific characteristics of male gender, patients with Medicare or Medicare with Medicaid, and patients in our General Internal Medicine clinic with a departed PCP were independent risk factors for hospital readmission. Patients with specific comorbidities were also at increased risk for admissions, including those with peripheral vascular disease, renal failure, lymphoma, fluid and electrolyte disorders, and anemia. Because this is the first study viewing readmissions from the perspective of primary care practices, our findings are unique in the literature. However, hospital‐based studies have found similar relationships between readmission rates and these specific comorbidities.[11, 12, 15] Unlike other studies, our study cohort did not show CHF as an independent risk factor. We hypothesize this is because in 2008, UCSF Medical Center introduced an inpatient‐based intervention to reduce readmissions in patients with heart failure. By 2011, the readmission rates of patients with heart failure (primary or secondary diagnoses) had dropped by 30%. The success of this program, focused only on patients with heart failure, likely affected our analysis of comorbidities as risk factors for readmissions.

Models developed to predict hospital readmissions, overall and for specific disease conditions, have inconsistently identified predictive factors, and there is not a specific set of variables that dominate.[16, 17, 18, 19] A recent review of readmission risk prediction models suggested that models that take into account psychosocial factors such as social support, substance abuse, and functional status improved model performance.[16] We hypothesize that the reason why male gender was significant in our model may be related to lack of social support, especially among those who may be single or widowed. Other studies have also showed male gender as a predictor for hospital readmissions.[14, 20]

People with Medicare as the primary payor or Medicare with Medicaid also tended to have higher risk of readmission. We believe that this may be a proxy for the combined effect of age, multiple comorbidities, and psychosocial factors. In a multicenter study of general medicine inpatients, Medicare, but not age, was also found to be a predictive variable.[21] Unlike other studies, our study did not find Medicaid alone as a significant predictive variable for readmissions.[15, 21] One explanation may be that in hospital‐based studies, people with Medicaid who are at high risk for readmission may be high‐risk because they do not have a PCP or good access to outpatient care. In our study, all patients have a PCP in 1 of the UCSF clinics, and access to care is improved with this established relationship. These other studies did not examine Medicare‐Medicaid dual eligibility status. Our results are consistent with a national study on avoidable hospital admissions that showed the dual eligibility population experiencing 60% higher avoidable admission rates compared to the Medicaid‐only population.[22]

An interesting finding was that there were no statistically significant differences in readmissions among patients who report a language other than English as their primary language. Although language barriers and health literacy can affect a patient's ability to understand discharge instructions, the use of translators at UCSF Medical Center may have decreased the risk of readmissions. For a small number of patients whose primary language was not recorded (unknown language in our model), they appeared to have a lower risk of readmissions. Language preferences are recorded by our admitting staff during the process of admission. This step may have been skipped after hours or if the patient was not able to answer the question. However, we do not have a good hypothesis as to why these patients may have a reduced risk of readmissions.

Having a departed PCP in the General Internal Medicine clinic was an independent predictor of readmissions. These patients have access to primary care; they can schedule acute appointments with covering providers for new medical issues or follow‐up of chronic conditions. However, until they are transferred to a new PCP, they do not have a provider who is proactively managing their preventive and chronic disease care, including follow‐up and coordinating care after hospital discharge. Our study is not the first to suggest adverse outcomes for patients who are in transition from 1 primary care provider to the next.[23] Studies conducted in General Internal Medicine clinics have shown missed opportunities for cancer screening and overlooked test results during the transition period,[24] and as many as one‐fifth of patients whom residents identified as high‐risk were lost to follow‐up.[25] However, our study is the first to show the link between PCP transition in a teaching clinic and hospital readmissions. This finding underscores the importance of continuity of care in the optimal management of patients following hospital discharge.

There are several limitations to this study. First, our study only considers hospitalizations to UCSF Medical Center, potentially undercounting readmissions to area hospitals. Because our study population are patients with PCPs at UCSF, these patients tend to seek specialty and acute care at UCSF Medical Center as well. We obtained payor data from our medical group (Hill Physicians), which covers our largest private payors, to understand whether our results can be applied on a global basis. We found that in 87.4% of index admissions with readmissions from January 1, 2010 through May 30, 2012, the readmission occurred at UCSF Medical Center. We conducted a similar analysis with CMS data from October 1, 2008 to June 30, 2011, the latest data we have from CMS. For patients with UCSF PCPs and a diagnosis of AMI or CHF, 100% were readmitted back to UCSF Medical Center. For patients with UCSF PCPs and a diagnosis of pneumonia, 89% were readmitted back to UCSF Medical Center. Given that only a small percentage of patients with PCPs at UCSF present for readmission at other area hospitals, we believe that limiting our analysis to UCSF Medical Center is reasonable.

In our study, we did not remove vaginal deliveries or Caesarian sections prior to building the model. Primary care physicians and their clinic leadership are accustomed to taking a population‐health perspective. We anticipate they would be interested in designing interventions and addressing readmission for the entire primary care panel. Although readmissions after delivery are not frequent, they can still occur, and interventions should not necessarily exclude this population. We did run a sensitivity analysis by removing vaginal delivery and Caesarian sections from the analysis. As expected, readmission rates for all practices increased except for Geriatrics. However, the independent predictors of readmissions did not change.

Our study is based on a population of patients at 1 urban academic medical center and may not be generalizable across all delivery systems. Our population is racially and ethnically diverse, and many do not speak English as their primary language. The study also spans different types of primary care clinics, capturing a wide range of ages and case mix. As PCP assignments fluctuate over time, there may be errors with PCP attribution in the UCSF Medical Center data systems. We do not believe errors in PCP attribution would differ across primary care practices. Because the primary care practices' performance reports on quality measures are based on PCP assignment, each clinic regularly updates their clinic panels and has specific protocols to address errors with PCP attribution.

Finally, our study includes only the variables that we were able to extract from administrative claims. Other explanatory variables that have been suggested as important for evaluation, such as social support, functional assessment, access to care, hospital discharge process, and posthospitalization follow‐up, were not included. Each of these could be explored in future studies.

This study offers a unique perspective of hospital readmission by introducing a new methodology for primary care clinics to calculate and evaluate their all‐cause 30‐day readmission rates. Although this methodology is not intended to provide real‐time feedback to clinics on readmitted patients, it opens the door for benchmarking based on specific case‐mix indices. Another direction for future research is to design robust evaluations of the impact of interventions, both inpatient and outpatient, on primary care clinic readmission rates. Finally, future research should replicate this analysis across teaching clinics to identify whether provider turnover is a consistent independent predictor of hospital readmissions.

This study also has implications for inpatient interventions. For example, discharging physicians may consider proactively identifying whether the patient has a continuous primary care provider. Patients who are in between PCPs may need closer follow‐up after discharge, until they re‐establish with a new PCP. This can be accomplished through a postdischarge clinic visit, either run by inpatient providers or covering physicians in the primary care clinic. In addition, the discharging physician may work with the case manager to increase the level of care coordination. The case manager could contact the primary care clinic and proactively ask for immediate PCP re‐assignment. Once a new PCP has been identified, the discharging physician could consider a warm hand‐off. With a warm hand‐off, the new PCP may feel more comfortable managing problems that may arise after hospital discharge, and especially before the first outpatient visit with the new PCP. Future research can test whether these interventions could effectively reduce hospital readmissions across a broad primary care population.

CONCLUSION

Primary care providers and their clinics play an important role in managing population health, decreasing healthcare spending, and keeping patients out of the hospital. In this study, we introduce a tool in which primary care clinics can begin to understand their hospital readmission rates. This may be particularly valuable as primary care providers enter global payment arrangements such as accountable care organizations or bundled payments and are responsible for a population of patients across the continuum of care. We found significant variation in readmission rates between different primary care practices, but much of this variation appears to be due to differences between practices in patient demographics, comorbidities, and hospitalization factors. Our study is the first to show the association between provider transitions and higher hospital readmissions. Continuity of care is critical for the optimal management of patients following hospital discharge. More attention will need to be focused on providing good continuity outpatient care for patients at high risk for readmissions.

Although potentially life saving, blood‐product transfusion is costly and associated with transfusion‐related adverse events, including death on rare occasions. Studies in varied patient populations have demonstrated that a restrictive red blood cell transfusion strategy reduces the number of transfusion‐related adverse effects and can result in improved short‐term survival.[1, 2, 3] In 2011, more than 20 million blood products were transfused in the United States, which resulted in more than 50,000 transfusion‐related adverse reactions (0.24%).[4] With a mean cost of greater than $50 per unit of plasma and $500 per unit of apheresis platelets,[4] the cost of blood transfusion is well in excess of $1 billion per year. Blood‐product transfusion is the most frequent inpatient procedure,[5] and inpatient blood‐product transfusion contributes to the bulk of transfusions nationwide. To study the utilization of blood‐product transfusion in the inpatient population, we studied the temporal trend of inpatient blood‐product transfusions in the United States from 2002 to 2011 using data from the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality.[4] The NIS, the largest inpatient care database in the United States, includes approximately a 20% stratified sample of US community hospital admissions and is weighted at discharge level to permit population‐level estimates.[6] We utilized this database to identify the total number of blood‐product transfusions and discharges between 2002 and 2011. We calculated the rate of all blood‐product transfusions, which include packed red blood cell, platelets, and other blood components, using the International Classification of DiseasesNinth Revision, Clinical Modification Procedural Clinical Classification Software code 222.[7] Trend analysis and calculation of average annual percent change were done using the Joinpoint Regression Program version 4.0.4 (National Cancer Institute, Bethesda, MD).[8] This software uses trend data and calculates the best fit lines to create the simplest joinpoint model that the data allow. The model can be expressed as a figure where several different multisegmented trend lines are connected together at the joinpoints. Trend over a fixed prespecified interval was computed as average annual percent change, and the Monte Carlo permutation method was used to test for apparent change in the trends.[9, 10] The study was exempted by the institutional review board of the University of Nebraska Medical Center.

Between 2002 and 2011, there were a total of 24,641,581 blood‐product transfusions among 389,761,571 hospitalizations. The rate of transfusion per 100 hospitalizations increased by 2.9% from 2002 to 2011 (4.6% in 2002 [n=1,767,111] to 7.5% in 2011 [n=2,929,312]) (Figure 1). The average annual percent change from 2002 to 2011 was 5.6% (95% confidence interval [CI]: 3.7‐7.6), which was statistically significant at P<0.05. A statistically significant change in trend (joinpoint) was observed in 2004. The annual percent change was 11.2% (95% CI: 0.323.4) from 2002 to 2004 and 4.1% (95% CI: 3.05.1) from 2004 to 2011, both of which were statistically significant at P<0.05 (Figure 2).

Figure 1

Figure 2

Our study demonstrates an overall increasing trend in the inpatient blood‐product transfusions over the past decade. However, the rate of increase seems to have slowed down since 2004. The National Blood Collection and Utilization Survey[4] demonstrated a decrease of 11.6% in the total number of all components transfused in the United States between 2008 and 2011. Our data are different from the survey, which also included blood transfusions in outpatient settings, emergency departments, and pediatric patients. The rising proportion of aging population with multiple comorbidities and cancers, increases in hematopoietic stem cell/solid organ transplants and chemotherapy, as well as widespread availability of blood products presumably contributed to the continued increase observed in our inpatient data after 2004. Nevertheless, the declining trend in the rate of the increased blood‐product transfusion usage seen after 2004 is encouraging. Increased awareness of restrictive transfusion strategy, coupled with efforts by professional bodies to improve the adoption of restrictive strategies, is most likely responsible for this.[3, 11, 12] As the clinical classification software procedure code 222 lumps together all the different types of blood products, we were unable to study the transfusion trend among each different type of blood products. In conclusion, further efforts need to be directed at increasing the awareness of clinicians, especially hospitalists, about the benefits of a restrictive transfusion policy and decreasing the rate of blood product use in the inpatient service. Furthermore, studies elaborating the patient population who are being transfused and the factors influencing the transfusion trends can provide useful insights to optimize blood‐product utilization and control resource consumption.

Although potentially life saving, blood‐product transfusion is costly and associated with transfusion‐related adverse events, including death on rare occasions. Studies in varied patient populations have demonstrated that a restrictive red blood cell transfusion strategy reduces the number of transfusion‐related adverse effects and can result in improved short‐term survival.[1, 2, 3] In 2011, more than 20 million blood products were transfused in the United States, which resulted in more than 50,000 transfusion‐related adverse reactions (0.24%).[4] With a mean cost of greater than $50 per unit of plasma and $500 per unit of apheresis platelets,[4] the cost of blood transfusion is well in excess of $1 billion per year. Blood‐product transfusion is the most frequent inpatient procedure,[5] and inpatient blood‐product transfusion contributes to the bulk of transfusions nationwide. To study the utilization of blood‐product transfusion in the inpatient population, we studied the temporal trend of inpatient blood‐product transfusions in the United States from 2002 to 2011 using data from the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality.[4] The NIS, the largest inpatient care database in the United States, includes approximately a 20% stratified sample of US community hospital admissions and is weighted at discharge level to permit population‐level estimates.[6] We utilized this database to identify the total number of blood‐product transfusions and discharges between 2002 and 2011. We calculated the rate of all blood‐product transfusions, which include packed red blood cell, platelets, and other blood components, using the International Classification of DiseasesNinth Revision, Clinical Modification Procedural Clinical Classification Software code 222.[7] Trend analysis and calculation of average annual percent change were done using the Joinpoint Regression Program version 4.0.4 (National Cancer Institute, Bethesda, MD).[8] This software uses trend data and calculates the best fit lines to create the simplest joinpoint model that the data allow. The model can be expressed as a figure where several different multisegmented trend lines are connected together at the joinpoints. Trend over a fixed prespecified interval was computed as average annual percent change, and the Monte Carlo permutation method was used to test for apparent change in the trends.[9, 10] The study was exempted by the institutional review board of the University of Nebraska Medical Center.

Between 2002 and 2011, there were a total of 24,641,581 blood‐product transfusions among 389,761,571 hospitalizations. The rate of transfusion per 100 hospitalizations increased by 2.9% from 2002 to 2011 (4.6% in 2002 [n=1,767,111] to 7.5% in 2011 [n=2,929,312]) (Figure 1). The average annual percent change from 2002 to 2011 was 5.6% (95% confidence interval [CI]: 3.7‐7.6), which was statistically significant at P<0.05. A statistically significant change in trend (joinpoint) was observed in 2004. The annual percent change was 11.2% (95% CI: 0.323.4) from 2002 to 2004 and 4.1% (95% CI: 3.05.1) from 2004 to 2011, both of which were statistically significant at P<0.05 (Figure 2).

Figure 1

Figure 2

Our study demonstrates an overall increasing trend in the inpatient blood‐product transfusions over the past decade. However, the rate of increase seems to have slowed down since 2004. The National Blood Collection and Utilization Survey[4] demonstrated a decrease of 11.6% in the total number of all components transfused in the United States between 2008 and 2011. Our data are different from the survey, which also included blood transfusions in outpatient settings, emergency departments, and pediatric patients. The rising proportion of aging population with multiple comorbidities and cancers, increases in hematopoietic stem cell/solid organ transplants and chemotherapy, as well as widespread availability of blood products presumably contributed to the continued increase observed in our inpatient data after 2004. Nevertheless, the declining trend in the rate of the increased blood‐product transfusion usage seen after 2004 is encouraging. Increased awareness of restrictive transfusion strategy, coupled with efforts by professional bodies to improve the adoption of restrictive strategies, is most likely responsible for this.[3, 11, 12] As the clinical classification software procedure code 222 lumps together all the different types of blood products, we were unable to study the transfusion trend among each different type of blood products. In conclusion, further efforts need to be directed at increasing the awareness of clinicians, especially hospitalists, about the benefits of a restrictive transfusion policy and decreasing the rate of blood product use in the inpatient service. Furthermore, studies elaborating the patient population who are being transfused and the factors influencing the transfusion trends can provide useful insights to optimize blood‐product utilization and control resource consumption.

Although potentially life saving, blood‐product transfusion is costly and associated with transfusion‐related adverse events, including death on rare occasions. Studies in varied patient populations have demonstrated that a restrictive red blood cell transfusion strategy reduces the number of transfusion‐related adverse effects and can result in improved short‐term survival.[1, 2, 3] In 2011, more than 20 million blood products were transfused in the United States, which resulted in more than 50,000 transfusion‐related adverse reactions (0.24%).[4] With a mean cost of greater than $50 per unit of plasma and $500 per unit of apheresis platelets,[4] the cost of blood transfusion is well in excess of $1 billion per year. Blood‐product transfusion is the most frequent inpatient procedure,[5] and inpatient blood‐product transfusion contributes to the bulk of transfusions nationwide. To study the utilization of blood‐product transfusion in the inpatient population, we studied the temporal trend of inpatient blood‐product transfusions in the United States from 2002 to 2011 using data from the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality.[4] The NIS, the largest inpatient care database in the United States, includes approximately a 20% stratified sample of US community hospital admissions and is weighted at discharge level to permit population‐level estimates.[6] We utilized this database to identify the total number of blood‐product transfusions and discharges between 2002 and 2011. We calculated the rate of all blood‐product transfusions, which include packed red blood cell, platelets, and other blood components, using the International Classification of DiseasesNinth Revision, Clinical Modification Procedural Clinical Classification Software code 222.[7] Trend analysis and calculation of average annual percent change were done using the Joinpoint Regression Program version 4.0.4 (National Cancer Institute, Bethesda, MD).[8] This software uses trend data and calculates the best fit lines to create the simplest joinpoint model that the data allow. The model can be expressed as a figure where several different multisegmented trend lines are connected together at the joinpoints. Trend over a fixed prespecified interval was computed as average annual percent change, and the Monte Carlo permutation method was used to test for apparent change in the trends.[9, 10] The study was exempted by the institutional review board of the University of Nebraska Medical Center.

Between 2002 and 2011, there were a total of 24,641,581 blood‐product transfusions among 389,761,571 hospitalizations. The rate of transfusion per 100 hospitalizations increased by 2.9% from 2002 to 2011 (4.6% in 2002 [n=1,767,111] to 7.5% in 2011 [n=2,929,312]) (Figure 1). The average annual percent change from 2002 to 2011 was 5.6% (95% confidence interval [CI]: 3.7‐7.6), which was statistically significant at P<0.05. A statistically significant change in trend (joinpoint) was observed in 2004. The annual percent change was 11.2% (95% CI: 0.323.4) from 2002 to 2004 and 4.1% (95% CI: 3.05.1) from 2004 to 2011, both of which were statistically significant at P<0.05 (Figure 2).

Figure 1

Figure 2

Our study demonstrates an overall increasing trend in the inpatient blood‐product transfusions over the past decade. However, the rate of increase seems to have slowed down since 2004. The National Blood Collection and Utilization Survey[4] demonstrated a decrease of 11.6% in the total number of all components transfused in the United States between 2008 and 2011. Our data are different from the survey, which also included blood transfusions in outpatient settings, emergency departments, and pediatric patients. The rising proportion of aging population with multiple comorbidities and cancers, increases in hematopoietic stem cell/solid organ transplants and chemotherapy, as well as widespread availability of blood products presumably contributed to the continued increase observed in our inpatient data after 2004. Nevertheless, the declining trend in the rate of the increased blood‐product transfusion usage seen after 2004 is encouraging. Increased awareness of restrictive transfusion strategy, coupled with efforts by professional bodies to improve the adoption of restrictive strategies, is most likely responsible for this.[3, 11, 12] As the clinical classification software procedure code 222 lumps together all the different types of blood products, we were unable to study the transfusion trend among each different type of blood products. In conclusion, further efforts need to be directed at increasing the awareness of clinicians, especially hospitalists, about the benefits of a restrictive transfusion policy and decreasing the rate of blood product use in the inpatient service. Furthermore, studies elaborating the patient population who are being transfused and the factors influencing the transfusion trends can provide useful insights to optimize blood‐product utilization and control resource consumption.

Interprofessional collaborative care (IPCC) involves members from different professions working together to enhance communication, coordination, and healthcare quality.[1, 2, 3] Because several current healthcare policy initiatives include financial incentives for increased quality of care, there has been resultant interest in the implementation of IPCC in healthcare systems.[4, 5] Unfortunately, many hospitals have found IPCC difficult to achieve. Hospital‐based medicine units are complex, time‐constrained environments requiring a high degree of collaboration and mutual decision‐making between nurses, physicians, therapists, pharmacists, care coordinators, and patients. In addition, despite recommendations for interprofessional collaborative care, the implementation and assessment of IPCC within this environment has not been well studied.[6, 7]

On academic internal medicine services, the majority of care decisions occur during rounds. Although rounds provide a common structure, the participants, length, location, and agenda of rounds tend to vary by institution and individual physician preference.[8, 9, 10, 11] Traditionally, ward rounds occur mostly in hallways and conference rooms rather than the patient's bedside.[12] Additionally, during rounds, nurse‐physician collaboration occurs infrequently, estimated at <10% of rounding time.[13] Recently, an increased focus on quality, safety, and collaboration has inspired the investigation and implementation of new methods to increase interprofessional collaboration during rounds, but many of these interventions occurred away from the patient's bedside.[14, 15] One trial of bedside interprofessional rounds (BIRs) by Curley et al. suggested improvements in patient‐level outcomes (cost and length of stay) versus traditional physician‐based rounds.[16] Although interprofessional nurse‐physician rounds at patients' bedsides may represent an ideal process, limited work has investigated this activity.[17]

A prerequisite for successful and sustained integration of BIRs is a shared conceptualization among physicians and nurses regarding the process. Such a shared conceptualization would include perceptions of benefits and barriers to implementation.[18] Currently, such perceptions have not been measured. In this study, we sought to evaluate perceptions of front‐line care providers on inpatient units, specifically nursing staff, attending physicians, and housestaff physicians, regarding the benefits and barriers to BIRs.

METHODS

RESULTS

Of the 171 surveys sent, 149 participants completed surveys (response rate 87%). Responses were received from 53/58 nursing staff (91% response), 21/28 attending physicians (75% response), and 75/85 housestaff physicians (88% response). Table 1 describes the participant response demographics.

DISCUSSION

In this study, we sought to compare perceptions of nurses and physicians on the benefits and barriers to BIRs. Nursing staff ranked each benefit higher than physicians, though rank orders of specific benefits were highly correlated. Highest‐ranked benefits related to coordination and communication more than quality or process benefits. Across groups, the highest‐ranked barriers to BIRs were related to time, whereas the lowest‐ranked factors were related to provider and patient discomfort. These results highlight important similarities and differences in perceptions between front‐line providers.

The highest‐ranked benefits were related to improved interprofessional communication and coordination. Combining interprofessional team members during care delivery allows for integrated understanding of daily care plans and clinical issues, and fosters collaboration and a team‐based atmosphere.[1, 20, 26] The lowest‐ranked benefits were related to more tangible measures, including length of stay, timely consultations, and judicious laboratory ordering. This finding contrasts with the limited literature demonstrating increased efficiency in general medicine units practicing IPCC.[16] These rankings may reflect a poor understanding or self‐assessment of outcome measures by healthcare providers, representing a potential focus for educational initiatives. Future investigations using objective assessment methods of outcomes and collaboration will provide a more accurate understanding of these findings.

The highest‐ranked barriers were related to time and systems issues. Several studies of physician‐based bedside rounds have identified systems‐ and time‐related issues as primary limiting barriers.[22, 24] In units without colocalization of patients and providers, finding receptive times for BIRs can be difficult. Although time‐related issues could be addressed by decreasing patient‐provider ratios, these changes require substantial investment in resources. A reasonable degree of improvement in efficiency and coordination is expected following acclimation to BIRs or by addressing modifiable systems factors to increase this activity. Less costly interventions, such as tailoring provider schedules, prescheduling patient rounding times, and geographic colocalization of patients and providers may be more feasible. However, the clinical microsystems within which medicine patients are cared for are often chaotic and disorganized at the infrastructural and cultural levels, which may be less influenced by surface‐level interventions. Such interventions may be ward specific and require customization to individual team needs.

The lowest‐ranked barriers to BIRs were related to provider‐ and patient‐related factors, including comfort level of patients and providers. Prior work on bedside rounds has identified physicians who are apprehensive about performing bedside rounds, but those who experience this activity are more likely to be comfortable with it.[12, 28] Our results from a culture where BIRs occur on nearly two‐thirds of patients suggest provider discomfort is not a predominant barrier.[22, 29] Additionally, educators have raised concerns about patient discomfort with bedside rounds, but nearly all studies evaluating patients' perspectives reveal patient preference for bedside case presentations over activities occurring in alternative locations.[30, 31, 32] Little work has investigated patient preference for BIRs as per our definition; our participants do not believe patients are discomforted by BIRs, building upon evidence in the literature for patient preferences regarding bedside activities.

Nursing staff perceptions of the benefits and culture related to BIRs were more positive than physicians. We hypothesize several reasons for this disparity. First, nursing staff may have more experience with observing and understanding the positive impact of BIRs and therefore are more likely to understand the positive ramifications. Alternatively, nursing staff may be satisfied with active integration into traditional physician‐centric decisions. Additionally, the professional culture and educational foundation of the nursing culture is based upon a patient‐centered approach and therefore may be more aligned with the goals of BIRs. Last, physicians may have competing priorities, favoring productivity and didactic learning rather than interprofessional collaboration. Further investigation is required to understand differences between nurses and physicians, in addition to other providers integral to BIRs (eg, care coordinators, pharmacists). Regardless, during the implementation of interprofessional collaborative care models, our findings suggest initial challenges, and the focus of educational initiatives may necessitate acclimating physician groups to benefits identified by front‐line nursing staff.

There are several limitations to our study. We investigated the perceptions of medicine nurses and physicians in 1 teaching hospital, limiting generalizability to other specialties, other vital professional groups, and nonteaching hospitals. Additionally, BIRs has been a focus of our hospital for several years. Therefore, perceived barriers may differ in BIRs‐nave hospitals. Second, although pilot‐tested for content, the construct validity of the instrument was not rigorously assessed, and the instrument was not designed to measure benefits and barriers not explicitly identified during pilot testing. Last, although surveys were anonymous, the possibility of social desirability bias exists, thereby limiting accuracy.

For over a century, physician‐led rounds have been the preferred modality for point‐of‐care decision making.[10, 15, 32, 33] BIRs address our growing understanding of patient‐centered care. Future efforts should address the quality of collaboration and current hospital and unit structures hindering patient‐centered IPCC and patient outcomes.

Interprofessional collaborative care (IPCC) involves members from different professions working together to enhance communication, coordination, and healthcare quality.[1, 2, 3] Because several current healthcare policy initiatives include financial incentives for increased quality of care, there has been resultant interest in the implementation of IPCC in healthcare systems.[4, 5] Unfortunately, many hospitals have found IPCC difficult to achieve. Hospital‐based medicine units are complex, time‐constrained environments requiring a high degree of collaboration and mutual decision‐making between nurses, physicians, therapists, pharmacists, care coordinators, and patients. In addition, despite recommendations for interprofessional collaborative care, the implementation and assessment of IPCC within this environment has not been well studied.[6, 7]

On academic internal medicine services, the majority of care decisions occur during rounds. Although rounds provide a common structure, the participants, length, location, and agenda of rounds tend to vary by institution and individual physician preference.[8, 9, 10, 11] Traditionally, ward rounds occur mostly in hallways and conference rooms rather than the patient's bedside.[12] Additionally, during rounds, nurse‐physician collaboration occurs infrequently, estimated at <10% of rounding time.[13] Recently, an increased focus on quality, safety, and collaboration has inspired the investigation and implementation of new methods to increase interprofessional collaboration during rounds, but many of these interventions occurred away from the patient's bedside.[14, 15] One trial of bedside interprofessional rounds (BIRs) by Curley et al. suggested improvements in patient‐level outcomes (cost and length of stay) versus traditional physician‐based rounds.[16] Although interprofessional nurse‐physician rounds at patients' bedsides may represent an ideal process, limited work has investigated this activity.[17]

A prerequisite for successful and sustained integration of BIRs is a shared conceptualization among physicians and nurses regarding the process. Such a shared conceptualization would include perceptions of benefits and barriers to implementation.[18] Currently, such perceptions have not been measured. In this study, we sought to evaluate perceptions of front‐line care providers on inpatient units, specifically nursing staff, attending physicians, and housestaff physicians, regarding the benefits and barriers to BIRs.

METHODS

RESULTS

Of the 171 surveys sent, 149 participants completed surveys (response rate 87%). Responses were received from 53/58 nursing staff (91% response), 21/28 attending physicians (75% response), and 75/85 housestaff physicians (88% response). Table 1 describes the participant response demographics.

DISCUSSION

In this study, we sought to compare perceptions of nurses and physicians on the benefits and barriers to BIRs. Nursing staff ranked each benefit higher than physicians, though rank orders of specific benefits were highly correlated. Highest‐ranked benefits related to coordination and communication more than quality or process benefits. Across groups, the highest‐ranked barriers to BIRs were related to time, whereas the lowest‐ranked factors were related to provider and patient discomfort. These results highlight important similarities and differences in perceptions between front‐line providers.

The highest‐ranked benefits were related to improved interprofessional communication and coordination. Combining interprofessional team members during care delivery allows for integrated understanding of daily care plans and clinical issues, and fosters collaboration and a team‐based atmosphere.[1, 20, 26] The lowest‐ranked benefits were related to more tangible measures, including length of stay, timely consultations, and judicious laboratory ordering. This finding contrasts with the limited literature demonstrating increased efficiency in general medicine units practicing IPCC.[16] These rankings may reflect a poor understanding or self‐assessment of outcome measures by healthcare providers, representing a potential focus for educational initiatives. Future investigations using objective assessment methods of outcomes and collaboration will provide a more accurate understanding of these findings.

The highest‐ranked barriers were related to time and systems issues. Several studies of physician‐based bedside rounds have identified systems‐ and time‐related issues as primary limiting barriers.[22, 24] In units without colocalization of patients and providers, finding receptive times for BIRs can be difficult. Although time‐related issues could be addressed by decreasing patient‐provider ratios, these changes require substantial investment in resources. A reasonable degree of improvement in efficiency and coordination is expected following acclimation to BIRs or by addressing modifiable systems factors to increase this activity. Less costly interventions, such as tailoring provider schedules, prescheduling patient rounding times, and geographic colocalization of patients and providers may be more feasible. However, the clinical microsystems within which medicine patients are cared for are often chaotic and disorganized at the infrastructural and cultural levels, which may be less influenced by surface‐level interventions. Such interventions may be ward specific and require customization to individual team needs.

The lowest‐ranked barriers to BIRs were related to provider‐ and patient‐related factors, including comfort level of patients and providers. Prior work on bedside rounds has identified physicians who are apprehensive about performing bedside rounds, but those who experience this activity are more likely to be comfortable with it.[12, 28] Our results from a culture where BIRs occur on nearly two‐thirds of patients suggest provider discomfort is not a predominant barrier.[22, 29] Additionally, educators have raised concerns about patient discomfort with bedside rounds, but nearly all studies evaluating patients' perspectives reveal patient preference for bedside case presentations over activities occurring in alternative locations.[30, 31, 32] Little work has investigated patient preference for BIRs as per our definition; our participants do not believe patients are discomforted by BIRs, building upon evidence in the literature for patient preferences regarding bedside activities.

Nursing staff perceptions of the benefits and culture related to BIRs were more positive than physicians. We hypothesize several reasons for this disparity. First, nursing staff may have more experience with observing and understanding the positive impact of BIRs and therefore are more likely to understand the positive ramifications. Alternatively, nursing staff may be satisfied with active integration into traditional physician‐centric decisions. Additionally, the professional culture and educational foundation of the nursing culture is based upon a patient‐centered approach and therefore may be more aligned with the goals of BIRs. Last, physicians may have competing priorities, favoring productivity and didactic learning rather than interprofessional collaboration. Further investigation is required to understand differences between nurses and physicians, in addition to other providers integral to BIRs (eg, care coordinators, pharmacists). Regardless, during the implementation of interprofessional collaborative care models, our findings suggest initial challenges, and the focus of educational initiatives may necessitate acclimating physician groups to benefits identified by front‐line nursing staff.

There are several limitations to our study. We investigated the perceptions of medicine nurses and physicians in 1 teaching hospital, limiting generalizability to other specialties, other vital professional groups, and nonteaching hospitals. Additionally, BIRs has been a focus of our hospital for several years. Therefore, perceived barriers may differ in BIRs‐nave hospitals. Second, although pilot‐tested for content, the construct validity of the instrument was not rigorously assessed, and the instrument was not designed to measure benefits and barriers not explicitly identified during pilot testing. Last, although surveys were anonymous, the possibility of social desirability bias exists, thereby limiting accuracy.

For over a century, physician‐led rounds have been the preferred modality for point‐of‐care decision making.[10, 15, 32, 33] BIRs address our growing understanding of patient‐centered care. Future efforts should address the quality of collaboration and current hospital and unit structures hindering patient‐centered IPCC and patient outcomes.

Interprofessional collaborative care (IPCC) involves members from different professions working together to enhance communication, coordination, and healthcare quality.[1, 2, 3] Because several current healthcare policy initiatives include financial incentives for increased quality of care, there has been resultant interest in the implementation of IPCC in healthcare systems.[4, 5] Unfortunately, many hospitals have found IPCC difficult to achieve. Hospital‐based medicine units are complex, time‐constrained environments requiring a high degree of collaboration and mutual decision‐making between nurses, physicians, therapists, pharmacists, care coordinators, and patients. In addition, despite recommendations for interprofessional collaborative care, the implementation and assessment of IPCC within this environment has not been well studied.[6, 7]

On academic internal medicine services, the majority of care decisions occur during rounds. Although rounds provide a common structure, the participants, length, location, and agenda of rounds tend to vary by institution and individual physician preference.[8, 9, 10, 11] Traditionally, ward rounds occur mostly in hallways and conference rooms rather than the patient's bedside.[12] Additionally, during rounds, nurse‐physician collaboration occurs infrequently, estimated at <10% of rounding time.[13] Recently, an increased focus on quality, safety, and collaboration has inspired the investigation and implementation of new methods to increase interprofessional collaboration during rounds, but many of these interventions occurred away from the patient's bedside.[14, 15] One trial of bedside interprofessional rounds (BIRs) by Curley et al. suggested improvements in patient‐level outcomes (cost and length of stay) versus traditional physician‐based rounds.[16] Although interprofessional nurse‐physician rounds at patients' bedsides may represent an ideal process, limited work has investigated this activity.[17]

A prerequisite for successful and sustained integration of BIRs is a shared conceptualization among physicians and nurses regarding the process. Such a shared conceptualization would include perceptions of benefits and barriers to implementation.[18] Currently, such perceptions have not been measured. In this study, we sought to evaluate perceptions of front‐line care providers on inpatient units, specifically nursing staff, attending physicians, and housestaff physicians, regarding the benefits and barriers to BIRs.

METHODS

RESULTS

Of the 171 surveys sent, 149 participants completed surveys (response rate 87%). Responses were received from 53/58 nursing staff (91% response), 21/28 attending physicians (75% response), and 75/85 housestaff physicians (88% response). Table 1 describes the participant response demographics.

DISCUSSION

In this study, we sought to compare perceptions of nurses and physicians on the benefits and barriers to BIRs. Nursing staff ranked each benefit higher than physicians, though rank orders of specific benefits were highly correlated. Highest‐ranked benefits related to coordination and communication more than quality or process benefits. Across groups, the highest‐ranked barriers to BIRs were related to time, whereas the lowest‐ranked factors were related to provider and patient discomfort. These results highlight important similarities and differences in perceptions between front‐line providers.

The highest‐ranked benefits were related to improved interprofessional communication and coordination. Combining interprofessional team members during care delivery allows for integrated understanding of daily care plans and clinical issues, and fosters collaboration and a team‐based atmosphere.[1, 20, 26] The lowest‐ranked benefits were related to more tangible measures, including length of stay, timely consultations, and judicious laboratory ordering. This finding contrasts with the limited literature demonstrating increased efficiency in general medicine units practicing IPCC.[16] These rankings may reflect a poor understanding or self‐assessment of outcome measures by healthcare providers, representing a potential focus for educational initiatives. Future investigations using objective assessment methods of outcomes and collaboration will provide a more accurate understanding of these findings.

The highest‐ranked barriers were related to time and systems issues. Several studies of physician‐based bedside rounds have identified systems‐ and time‐related issues as primary limiting barriers.[22, 24] In units without colocalization of patients and providers, finding receptive times for BIRs can be difficult. Although time‐related issues could be addressed by decreasing patient‐provider ratios, these changes require substantial investment in resources. A reasonable degree of improvement in efficiency and coordination is expected following acclimation to BIRs or by addressing modifiable systems factors to increase this activity. Less costly interventions, such as tailoring provider schedules, prescheduling patient rounding times, and geographic colocalization of patients and providers may be more feasible. However, the clinical microsystems within which medicine patients are cared for are often chaotic and disorganized at the infrastructural and cultural levels, which may be less influenced by surface‐level interventions. Such interventions may be ward specific and require customization to individual team needs.

The lowest‐ranked barriers to BIRs were related to provider‐ and patient‐related factors, including comfort level of patients and providers. Prior work on bedside rounds has identified physicians who are apprehensive about performing bedside rounds, but those who experience this activity are more likely to be comfortable with it.[12, 28] Our results from a culture where BIRs occur on nearly two‐thirds of patients suggest provider discomfort is not a predominant barrier.[22, 29] Additionally, educators have raised concerns about patient discomfort with bedside rounds, but nearly all studies evaluating patients' perspectives reveal patient preference for bedside case presentations over activities occurring in alternative locations.[30, 31, 32] Little work has investigated patient preference for BIRs as per our definition; our participants do not believe patients are discomforted by BIRs, building upon evidence in the literature for patient preferences regarding bedside activities.

Nursing staff perceptions of the benefits and culture related to BIRs were more positive than physicians. We hypothesize several reasons for this disparity. First, nursing staff may have more experience with observing and understanding the positive impact of BIRs and therefore are more likely to understand the positive ramifications. Alternatively, nursing staff may be satisfied with active integration into traditional physician‐centric decisions. Additionally, the professional culture and educational foundation of the nursing culture is based upon a patient‐centered approach and therefore may be more aligned with the goals of BIRs. Last, physicians may have competing priorities, favoring productivity and didactic learning rather than interprofessional collaboration. Further investigation is required to understand differences between nurses and physicians, in addition to other providers integral to BIRs (eg, care coordinators, pharmacists). Regardless, during the implementation of interprofessional collaborative care models, our findings suggest initial challenges, and the focus of educational initiatives may necessitate acclimating physician groups to benefits identified by front‐line nursing staff.

There are several limitations to our study. We investigated the perceptions of medicine nurses and physicians in 1 teaching hospital, limiting generalizability to other specialties, other vital professional groups, and nonteaching hospitals. Additionally, BIRs has been a focus of our hospital for several years. Therefore, perceived barriers may differ in BIRs‐nave hospitals. Second, although pilot‐tested for content, the construct validity of the instrument was not rigorously assessed, and the instrument was not designed to measure benefits and barriers not explicitly identified during pilot testing. Last, although surveys were anonymous, the possibility of social desirability bias exists, thereby limiting accuracy.

For over a century, physician‐led rounds have been the preferred modality for point‐of‐care decision making.[10, 15, 32, 33] BIRs address our growing understanding of patient‐centered care. Future efforts should address the quality of collaboration and current hospital and unit structures hindering patient‐centered IPCC and patient outcomes.